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TECHNICAL PAPERS

Bi-Objective Optimization Design of Heterogeneous Injection Mold Cooling Systems

[+] Author and Article Information
Jinhua Huang, Georges M. Fadel

Department of Mechanical Engineering Clemson University Clemson, SC 29634-0921

J. Mech. Des 123(2), 226-239 (Feb 01, 2000) (14 pages) doi:10.1115/1.1347992 History: Received February 01, 2000
Copyright © 2001 by ASME
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References

Figures

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The averaged cyclic, transient cavity surface temperature
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Directions for heat flow between the polymer part and a cooling channel during the steady cyclic cooling period
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Closed small area between the dashed lines and the boundary of the polymer part, a must area for removed heat to go through
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Sensitive area formed by common tangents between the polymer part and the cooling channels
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Flow diagram of the two-step algorithm
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Geometry and dimensions of the polymer part used in case study I
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A schematic view of mold assembly for cooling analysis for case study I: 1 parting line, 2 upper die, 3 cooling channel, 4 polymer part, 5 lower die.
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Definition of continuous coordinate design variables, geometric constraint description, and cooling channel ordering
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Two-dimensional mesh grids from step one with polymer (1) as the part material in case study I: (a) before optimization and after optimization with objective functions related by the weights: (b) w=0, (c) w=0.5, (d) w=1.
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Two-dimensional mesh grids from step one with polymer (2) as the part material in case study I: (a) before optimization and after optimization by objective functions relating to (b) w=0, (c) w=0.5, (d) w=1.
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Two-dimensional part temperature distribution from step one with polymer (1) as the part material in case study I: (a) before optimization and after optimization by objective functions relating to (b) w=0, (c) w=0.5, (d) w=1.
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Two-dimensional part temperature distribution from step one with polymer (2) as the part material in case study I: (a) before optimization and after optimization by objective functions relating to (b) w=0, (c) w=0.5, (d) w=1.
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Two-dimensional part temperature distribution from step two with polymer (2) as the part material and ceramics as the secondary mold material in case study I: (a) before any optimization (step one) and after step two by objective functions relating to (b) w=0, population size=50,generation=5000 (c) w=0.5, population size=50,generation=2500 (d) w=1, population size=50,generation=1000.
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Two-dimensional part temperature distribution from step two with polymer (2) as the part material and bronze as the secondary mold material in case study I: (a) before any optimization (step one) and after step two by objective functions relating to (b) w=0, population size=50,generation=1000 (c) w=0.5, population size=50,generation=2500 (d) w=1, population size=50,generation=2000.
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Ceramics distributions corresponding to Fig. 13.
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Bronze distributions corresponding to Fig. 14.
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Geometry and dimensions of the polymer part used in case study II
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A schematic view of mold assembly for cooling analysis for case study II
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Two-dimensional part temperature contours for case study II relating to (a) Aluminum as the single mold material and (b) Mullite as the single mold material
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Optimal two-dimensional part temperature contours for case study II relating to (a) w=0, population size=100,generation=14,000 and (b) w=0.3333, population size=100,generation=8,000 (c) w=0.6667, population size=100,generation=8,000 (d) w=1.0, population size=100,generation=2,000
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Mullite distributions corresponding to Fig. 20
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Bi-objective Pareto curve for case study II
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Smoother temperature contours corresponding to Fig. 13(b), Fig. 13(c), Fig. 14(a), Fig. 14(c), and Fig. 14(d)

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